A Statistical Approach to Continuous Self-Calibrating Eye Gaze Tracking for Head-Mounted Virtual Reality Systems
This addresses the inconvenience of calibration for VR users, though it is incremental as it builds on existing eye tracking methods.
The paper tackles the problem of eye gaze tracking in head-mounted VR systems by introducing a continuous self-calibrating algorithm that eliminates explicit calibration steps, achieving accuracy nearly as good as explicit calibration.
We present a novel, automatic eye gaze tracking scheme inspired by smooth pursuit eye motion while playing mobile games or watching virtual reality contents. Our algorithm continuously calibrates an eye tracking system for a head mounted display. This eliminates the need for an explicit calibration step and automatically compensates for small movements of the headset with respect to the head. The algorithm finds correspondences between corneal motion and screen space motion, and uses these to generate Gaussian Process Regression models. A combination of those models provides a continuous mapping from corneal position to screen space position. Accuracy is nearly as good as achieved with an explicit calibration step.